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Process from counts matrix to ready for downstream analysis

In this phase we do final filtering, binarization, TF-IDF, SVD, UMAP for scATAC data based on binarized count over peaks identified over clades in phases 1-3, extract transcript expression signal from scATAC data, plot marker genes, find tentative cluster ids, integrate with scRNA etc…

plan("multisession", workers = 6)
options(future.globals.maxSize = 12 * 1024 ^ 3)
#setwd("/Volumes/ExtSSD/ExtraWorkspace/E12R1/scATAC_data/")
sample.name <- "E12_R1"
run.date <- "240322"
# Reading scRNA data rds object
#s.data_rna <- readRDS("../scRNA/E14_DI_scRNAseq_neurons_clean.rds")
#cluster_names<-read_tsv("../scRNA/E14_DI_scRNAseq_cleaned_top20_allclusters.csv")
#cluster_id_name <- distinct(cluster_names[,c("cluster","ClusterName")])->cluster_id_name
#scRNA_clean_markers_file <- "../scRNA/E14_DI_scRNAseq_cleaned_top20_allclusters.csv"
s.data <- readRDS(paste("../scATAC_data/",sample.name,".merged.peaks.271021.Rds",sep=""))
s.data_RNA <- readRDS("../scRNA_data/e12_ens_whole_rescaled.Rds")
Warning in gzfile(file, "rb") :
  cannot open compressed file '../scRNA_data/e12_ens_whole_rescaled.Rds', probable reason 'No such file or directory'
Error in gzfile(file, "rb") : cannot open the connection
DefaultAssay(s.data) <- 'peaks'

set.seed(2020)

s.data <- RunTFIDF(s.data, method=3)
Performing TF-IDF normalization
s.data <- FindTopFeatures(s.data, min.cutoff = 'q25')
s.data <- RunSVD(
  object = s.data,
  assay = 'peaks',
  reduction.key = 'LSI_',
  reduction.name = 'lsi'
)
Running SVD
Scaling cell embeddings

Visualizations

Read depth and dimension reduction component correlation

DepthCor(s.data)


Plotting read depth correlation plot, in Seurat pipeline usually first PC1 is omitted because of this association.

scATAC UMAP clusters

s.data <- RunUMAP(object = s.data, reduction = 'lsi', dims = 2:30, min.dist=0.05, spread=1.1)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
15:06:16 UMAP embedding parameters a = 1.506 b = 0.8373
15:06:16 Read 4952 rows and found 29 numeric columns
15:06:16 Using Annoy for neighbor search, n_neighbors = 30
15:06:16 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:06:17 Writing NN index file to temp file /var/folders/yb/wvk8t07s719136klxnc8nmp9sglf93/T//RtmpDm8Ztu/filef67927c6cd96
15:06:17 Searching Annoy index using 6 threads, search_k = 3000
15:06:17 Annoy recall = 100%
15:06:18 Commencing smooth kNN distance calibration using 6 threads
15:06:20 Initializing from normalized Laplacian + noise
15:06:21 Commencing optimization for 500 epochs, with 187612 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:06:28 Optimization finished
s.data <- FindNeighbors(object = s.data, reduction = 'lsi', dims = 2:30)
Computing nearest neighbor graph
Computing SNN
s.data <- FindClusters(object = s.data, verbose = FALSE, algorithm = 4, resolution = 1)
DimPlot(object = s.data, label = TRUE, pt.size=1.2) + NoLegend()

DimPlot(object = s.data, label = TRUE, pt.size=.5,group.by="replicate", shuffle =TRUE) + NoLegend()

# Compute gene activities
# Now that scRNA data has been processed with ENSMUSG ids this now longer works directly like this.
# Let's extract genomic annotation metadata from s.data[['peaks']]
genomic.metadata <- mcols(Annotation(s.data[['peaks']]))
# Generate conversion table from gene_name to gene_id
gene_name2gene_id <- as_tibble(genomic.metadata[,c("gene_name","gene_id")])

# Calculate gene activity estimate from scATAC reads based on the scATAC, by using gene_names as function does not support any other id
gene.activities <- GeneActivity(s.data, assay="peaks")
Extracting gene coordinates
Extracting reads overlapping genomic regions
Extracting reads overlapping genomic regions
# Store gene_names
gene.names <- rownames(gene.activities)

# Switch sparse matrix to use ensmusg id
ensmusg.ids <- gene_name2gene_id[match(gene.names,pull(gene_name2gene_id,"gene_name")),] %>% pull("gene_id")
gene_names <- gene_name2gene_id[match(gene.names,pull(gene_name2gene_id,"gene_name")),] %>% pull("gene_name")

# Dropping NAs
non.na.i <- !is.na(ensmusg.ids)
gene.activities.gene_id <- gene.activities[non.na.i,]
rownames(gene.activities.gene_id) <- ensmusg.ids[non.na.i]

# Add the gene activity matrix to the Seurat object as a new assay
s.data[['Activity']] <- CreateAssayObject(counts = gene.activities.gene_id)
s.data <- NormalizeData(
  object = s.data,
  assay = 'Activity',
  normalization.method = 'LogNormalize',
  scale.factor = median(s.data$nCount_Activity)
)
# Add gene_name to gene_id mapping into the s.data[['Activity']] assays metadata
s.data[['Activity']]<- AddMetaData(s.data[['Activity']], col.name = "feature_symbol", metadata = gene_names[non.na.i])

scATAC clades projection into UMAP clusters

#DimPlot(object = s.data, label = TRUE, pt.size=1.2,group.by="clade") + NoLegend()

Read marker gene list and do FeaturePlot

neuronal.markers<- read_tsv("../../CellAnnotation/E12.5_cluster_markers_for_ATACseq.txt", col_names = c("annotation","geneName"))
Rows: 73 Columns: 2
── Column specification ──────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): annotation, geneName

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
feature.metadata <- s.data[['Activity']][[]] %>% rownames_to_column(var="gene_id") %>% as_tibble()
neuronal.markers.tmp <- filter(feature.metadata, feature_symbol %in% neuronal.markers$geneName)

neuronal.markers.ids <- pull(neuronal.markers.tmp,"gene_id")
neuronal.markers.names <- pull(neuronal.markers.tmp,"feature_symbol")

DefaultAssay(s.data) <- 'Activity'

f.plot.tmp <- FeaturePlot(
  object = s.data,
  features = neuronal.markers.ids,
  pt.size = 0.1,
  max.cutoff = 'q95',
  combine = F
)

f.plots.1 <- lapply(1:length(f.plot.tmp),function(i){
  f.plot.tmp[[i]] + labs(title=neuronal.markers.names[i])
})

patchwork::wrap_plots(f.plots.1)


Plot of some marker genes with signal from 2kbp upstream and only from those feature regions (peaks) that form clusters seen in UMAP

Perform integration with RNA sample

morello_e12_clusters <- read_tsv("../scRNA_data/Morello_et_al_cluster_anno.tsv")
Rows: 42 Columns: 2
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (1): clusterName
dbl (1): clusterNumber

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
morello.i <- match(as.vector(s.data_RNA@meta.data$seurat_clusters),morello_e12_clusters$clusterNumber)

s.data_RNA <- AddMetaData(s.data_RNA, pull(morello_e12_clusters[morello.i,2], clusterName), col.name = "clusterAnnotation")
# Converting numerical Seurat clusters to alphabeticals and store in $CellType slot of s.data_rna

# Finding transfer anchors
transfer.anchors <- FindTransferAnchors(
    reference = s.data_RNA,
    query = s.data,
    reduction = 'cca',
    reference.assay="RNA",
    query.assay = "Activity",
    features = VariableFeatures(object=s.data_RNA)
)
Warning: 335 features of the features specified were not present in both the reference query assays. 
Continuing with remaining 2665 features.
Warning in RunCCA.Seurat(object1 = reference, object2 = query, features = features,  :
  Running CCA on different assays
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 11260 anchors
Filtering anchors
    Retained 7005 anchors
# TODO: Find out which object and why there are graphs(4?) without associated assays, this however based on googling doesn't show as meaningful warning()

predicted.labels <- TransferData(
  anchorset = transfer.anchors,
  refdata = s.data_RNA@meta.data$clusterAnnotation,
  weight.reduction = s.data[['lsi']],
  dims = 2:30
)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
s.data <- AddMetaData(object = s.data, metadata = predicted.labels)

Label transfer, following and adapting https://satijalab.org/seurat/v3.0/atacseq_integration_vignette.html

Plotting prediction scores


Histogram of prediction scores, in Satija lab integration process each cell get prediction score how well it can be mapped from dataspace to another. Values > 0.5 are considered to be “good”.

Fraction of cells mapping acceptable between scATAC and scRNA data spaces

prediction.score.over.th <- table(s.data$prediction.score.max > 0.5)
p.freq <- prediction.score.over.th['TRUE']/(prediction.score.over.th['TRUE']+prediction.score.over.th['FALSE'])

p.score.th <- as.numeric(prediction.score.over.th)
val_names <- sprintf("%s (%s)", c("Match not found", "Match found"), scales::percent(round(p.score.th/sum(p.score.th), 2)))
names(p.score.th) <- val_names
waffle::waffle(p.score.th/10,colors = c("#fb8072", "#8dd3c7", "white"), rows=9, size=1)


Waffle plot visualizing proportions of properly mapping cells

Label transfer visualization

#' Select only cells with prediction score over 0.5
s.data.filtered <- subset(s.data, subset = prediction.score.max > 0.5)

#' To make the colors match, TODO: Check why there are few NAs in the predicted ids
#s.data.filtered$predicted.id <- factor(s.data.filtered$predicted.id, levels = letters[as.numeric(levels(s.data_rna))]) 

# Do combined plotting
p1 <- DimPlot(s.data.filtered, group.by = "predicted.id", label = TRUE, repel = TRUE, label.size=7, pt.size=2) + NoLegend() + scale_colour_hue(drop = FALSE)
p2 <- DimPlot(s.data_RNA, group.by = "clusterAnnotation", label = TRUE, repel = TRUE, label.size=7) + NoLegend()

p1 + ggtitle("scATAC-seq cells, labels predicted from scRNA")

p2 + ggtitle("scRNA-seq cells")


Filtering for cells mapping properly and visualizing cluster labels from scRNA (right side) to scATAC (left side).

Perform RNA data imputation into scATAC cells

Finding integration vectors

Finding integration vector weights

Transfering 27999 features onto reference data

Centering data matrix

11:43:23 UMAP embedding parameters a = 0.9922 b = 1.112

11:43:23 Read 10354 rows and found 29 numeric columns

11:43:23 Using Annoy for neighbor search, n_neighbors = 30

11:43:23 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

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11:43:28 Writing NN index file to temp file /var/folders/yb/wvk8t07s719136klxnc8nmp9sglf93/T//Rtmpnq8Zdj/file72653e114263

11:43:28 Searching Annoy index using 1 thread, search_k = 3000

11:43:32 Annoy recall = 100%

11:43:32 Commencing smooth kNN distance calibration using 1 thread

11:43:35 Initializing from normalized Laplacian + noise

11:43:35 Commencing optimization for 200 epochs, with 432826 positive edges

11:43:45 Optimization finished

Coembed dataset plotting

p1 <- DimPlot(coembed, group.by = "tech")
p2 <- DimPlot(coembed, group.by = "clusterAnnotation", label = TRUE, repel = TRUE)  + theme(legend.position="none") 

p1 + p2


Coembed plotting scATAC and scRNA cells together. Usable mainly for validation purposes.

s.data <- subset(s.data, subset = prediction.score.max > 0.5)
DefaultAssay(s.data) <- "peaks"
s.data <- RunUMAP(object = s.data, reduction = 'lsi', dims = 2:30, spread=1.4)
15:30:06 UMAP embedding parameters a = 0.6151 b = 1.019
15:30:06 Read 4329 rows and found 29 numeric columns
15:30:06 Using Annoy for neighbor search, n_neighbors = 30
15:30:06 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:30:07 Writing NN index file to temp file /var/folders/yb/wvk8t07s719136klxnc8nmp9sglf93/T//RtmpDm8Ztu/filef679522af9b5
15:30:07 Searching Annoy index using 6 threads, search_k = 3000
15:30:07 Annoy recall = 100%
15:30:11 Commencing smooth kNN distance calibration using 6 threads
15:30:14 Initializing from normalized Laplacian + noise
15:30:14 Commencing optimization for 500 epochs, with 161262 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:30:20 Optimization finished
s.data <- FindNeighbors(object = s.data, reduction = 'lsi', dims = 2:30)
Computing nearest neighbor graph
Computing SNN
s.data <- FindClusters(object = s.data, verbose = FALSE, algorithm = 4, resolution = 1)
DimPlot(object = s.data, label = TRUE, pt.size=1.2) + NoLegend()

Plot marker gene FeaturePlots with imputed scRNA data

neuronal.markers<- read_tsv("../../CellAnnotation/E12.5_cluster_markers_for_ATACseq.txt", col_names = c("annotation","geneName"))
Rows: 73 Columns: 2
── Column specification ──────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): annotation, geneName

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
feature.metadata <- s.data[['Activity']][[]] %>% rownames_to_column(var="gene_id") %>% as_tibble()
neuronal.markers.tmp <- filter(feature.metadata, feature_symbol %in% neuronal.markers$geneName)

neuronal.markers.ids <- pull(neuronal.markers.tmp,"gene_id")
neuronal.markers.names <- pull(neuronal.markers.tmp,"feature_symbol")
DefaultAssay(s.data) <- 'RNA'

f.plot.tmp <- FeaturePlot(
  object = s.data,
  features = neuronal.markers.ids,
  pt.size = 0.1,
  max.cutoff = 'q95',
  combine = F
)

f.plots.2 <- lapply(1:length(f.plot.tmp),function(i){
  f.plot.tmp[[i]] + labs(title=neuronal.markers.names[i])
})

patchwork::wrap_plots(f.plots.2)


Plot marker gene FeaturePlots with imputed scRNA data

Plot gini index based validation of clustering effectiveness

# Define a set of HK genes
hk.genes <- c("RRN18S","Actb","Gapdh","Pgk1","Ppia","Rpl13a","Rplp0","Arbp","B2M","Ywhaz","Sdha","Tfrc","Gusb","Hmbs","Hprt1","Tbp")

hk.genes.id <- convert_feature_identity(s.data, "RNA",features = hk.genes)
[1] "Instance: Found matching for 12 features out of total 16 provided features"
neuronal.markers.id <- convert_feature_identity(s.data, "RNA",features = neuronal.markers$geneName)
[1] "Instance: Found matching for 71 features out of total 73 provided features"
# Neuronal marker mean per cluster in Cusanovich data
gene.i <- match(neuronal.markers.id,s.data[['RNA']]@data@Dimnames[[1]])
gene.i<-gene.i[!is.na(gene.i)]
barcode.clusters <- s.data@meta.data$seurat_clusters
marker.matrix <- s.data[['RNA']]@data[gene.i,]
marker.tb <- as_tibble(t(as.data.frame(marker.matrix)))
marker.tb<-tibble(marker.tb,cluster=barcode.clusters)
marker.mean <- list()
marker.mean$mean.by.cluster <- marker.tb %>% group_by(cluster) %>% summarize_all(mean)

# HK gene mean per cluster in Cusanovich data
gene.i <- match(hk.genes.id,s.data[['RNA']]@data@Dimnames[[1]])
gene.i<-gene.i[!is.na(gene.i)]
barcode.clusters <- s.data@meta.data$seurat_clusters
marker.matrix <- s.data[['RNA']]@data[gene.i,]
marker.tb <- as_tibble(t(as.data.frame(marker.matrix)))
marker.tb<-tibble(marker.tb,cluster=barcode.clusters)
hk.mean <- list()
hk.mean$mean.by.cluster <- marker.tb %>% group_by(cluster) %>% summarize_all(mean)

# Gini indeces for Cusanovich data
cus.hk.gini <- apply(hk.mean$mean.by.cluster[,-1],2,gini)
cus.neur.gini <- apply(marker.mean$mean.by.cluster[,-1],2,gini)

gini.tb<-tibble(gini.index=c(cus.hk.gini,cus.neur.gini),type=c(rep("hk",length(cus.hk.gini)),rep("neur",length(cus.neur.gini)))) %>%  dplyr::filter(!is.na(gini.index))

ggplot(gini.tb, aes(x=gini.index,y=type,fill="blue"))+geom_boxplot(fill="lightblue")+ theme(legend.position="none") + theme_classic() 


Gini index based validation of clustering.

Adding Motif information into the object

Hocomocov11 <- read_jaspar("../../mm10/HOCOMOCOv11_core_MOUSE_mono_jaspar_format.txt")
names(Hocomocov11) <- lapply(Hocomocov11,function(x){x@name})
Hocomocov11 <- convert_motifs(Hocomocov11, "TFBSTools-PWMatrix")
Note: motif [AHR_M..] has an empty nsites slot, using 100.
Note: motif [AIRE_..] has an empty nsites slot, using 100.
Note: motif [ALX1_..] has an empty nsites slot, using 100.
Note: motif [ANDR_..] has an empty nsites slot, using 100.
Note: motif [AP2A_..] has an empty nsites slot, using 100.
Note: motif [AP2B_..] has an empty nsites slot, using 100.
Note: motif [AP2C_..] has an empty nsites slot, using 100.
Note: motif [ARI5B..] has an empty nsites slot, using 100.
Note: motif [ARNT_..] has an empty nsites slot, using 100.
Note: motif [ASCL1..] has an empty nsites slot, using 100.
Note: motif [ASCL2..] has an empty nsites slot, using 100.
Note: motif [ATF1_..] has an empty nsites slot, using 100.
Note: motif [ATF2_..] has an empty nsites slot, using 100.
Note: motif [ATF3_..] has an empty nsites slot, using 100.
Note: motif [ATF4_..] has an empty nsites slot, using 100.
Note: motif [ATOH1..] has an empty nsites slot, using 100.
Note: motif [BACH1..] has an empty nsites slot, using 100.
Note: motif [BACH2..] has an empty nsites slot, using 100.
Note: motif [BARX1..] has an empty nsites slot, using 100.
Note: motif [BATF3..] has an empty nsites slot, using 100.
Note: motif [BATF_..] has an empty nsites slot, using 100.
Note: motif [BCL6_..] has an empty nsites slot, using 100.
Note: motif [BHA15..] has an empty nsites slot, using 100.
Note: motif [BHE40..] has an empty nsites slot, using 100.
Note: motif [BMAL1..] has an empty nsites slot, using 100.
Note: motif [BRAC_..] has an empty nsites slot, using 100.
Note: motif [CDX1_..] has an empty nsites slot, using 100.
Note: motif [CDX2_..] has an empty nsites slot, using 100.
Note: motif [CEBPA..] has an empty nsites slot, using 100.
Note: motif [CEBPB..] has an empty nsites slot, using 100.
Note: motif [CEBPD..] has an empty nsites slot, using 100.
Note: motif [CEBPE..] has an empty nsites slot, using 100.
Note: motif [CEBPG..] has an empty nsites slot, using 100.
Note: motif [CLOCK..] has an empty nsites slot, using 100.
Note: motif [COE1_..] has an empty nsites slot, using 100.
Note: motif [COT1_..] has an empty nsites slot, using 100.
Note: motif [COT2_..] has an empty nsites slot, using 100.
Note: motif [CREB1..] has an empty nsites slot, using 100.
Note: motif [CREM_..] has an empty nsites slot, using 100.
Note: motif [CRX_M..] has an empty nsites slot, using 100.
Note: motif [CTCFL..] has an empty nsites slot, using 100.
Note: motif [CTCF_..] has an empty nsites slot, using 100.
Note: motif [CUX1_..] has an empty nsites slot, using 100.
Note: motif [CUX2_..] has an empty nsites slot, using 100.
Note: motif [DBP_M..] has an empty nsites slot, using 100.
Note: motif [DDIT3..] has an empty nsites slot, using 100.
Note: motif [DLX3_..] has an empty nsites slot, using 100.
Note: motif [DLX5_..] has an empty nsites slot, using 100.
Note: motif [DMRT1..] has an empty nsites slot, using 100.
Note: motif [DMRTB..] has an empty nsites slot, using 100.
Note: motif [E2F1_..] has an empty nsites slot, using 100.
Note: motif [E2F2_..] has an empty nsites slot, using 100.
Note: motif [E2F3_..] has an empty nsites slot, using 100.
Note: motif [E2F4_..] has an empty nsites slot, using 100.
Note: motif [E2F5_..] has an empty nsites slot, using 100.
Note: motif [E2F6_..] has an empty nsites slot, using 100.
Note: motif [E2F7_..] has an empty nsites slot, using 100.
Note: motif [EGR1_..] has an empty nsites slot, using 100.
Note: motif [EGR2_..] has an empty nsites slot, using 100.
Note: motif [EHF_M..] has an empty nsites slot, using 100.
Note: motif [ELF1_..] has an empty nsites slot, using 100.
Note: motif [ELF2_..] has an empty nsites slot, using 100.
Note: motif [ELF3_..] has an empty nsites slot, using 100.
Note: motif [ELF5_..] has an empty nsites slot, using 100.
Note: motif [ELK1_..] has an empty nsites slot, using 100.
Note: motif [ELK4_..] has an empty nsites slot, using 100.
Note: motif [EPAS1..] has an empty nsites slot, using 100.
Note: motif [ERG_M..] has an empty nsites slot, using 100.
Note: motif [ERR1_..] has an empty nsites slot, using 100.
Note: motif [ERR2_..] has an empty nsites slot, using 100.
Note: motif [ERR3_..] has an empty nsites slot, using 100.
Note: motif [ESR1_..] has an empty nsites slot, using 100.
Note: motif [ESR2_..] has an empty nsites slot, using 100.
Note: motif [ETS1_..] has an empty nsites slot, using 100.
Note: motif [ETS2_..] has an empty nsites slot, using 100.
Note: motif [ETV2_..] has an empty nsites slot, using 100.
Note: motif [ETV4_..] has an empty nsites slot, using 100.
Note: motif [ETV6_..] has an empty nsites slot, using 100.
Note: motif [EVI1_..] has an empty nsites slot, using 100.
Note: motif [FEV_M..] has an empty nsites slot, using 100.
Note: motif [FLI1_..] has an empty nsites slot, using 100.
Note: motif [FOSB_..] has an empty nsites slot, using 100.
Note: motif [FOSL1..] has an empty nsites slot, using 100.
Note: motif [FOSL2..] has an empty nsites slot, using 100.
Note: motif [FOS_M..] has an empty nsites slot, using 100.
Note: motif [FOXA1..] has an empty nsites slot, using 100.
Note: motif [FOXA2..] has an empty nsites slot, using 100.
Note: motif [FOXA3..] has an empty nsites slot, using 100.
Note: motif [FOXC1..] has an empty nsites slot, using 100.
Note: motif [FOXD3..] has an empty nsites slot, using 100.
Note: motif [FOXI1..] has an empty nsites slot, using 100.
Note: motif [FOXJ2..] has an empty nsites slot, using 100.
Note: motif [FOXJ3..] has an empty nsites slot, using 100.
Note: motif [FOXK1..] has an empty nsites slot, using 100.
Note: motif [FOXL2..] has an empty nsites slot, using 100.
Note: motif [FOXM1..] has an empty nsites slot, using 100.
Note: motif [FOXO1..] has an empty nsites slot, using 100.
Note: motif [FOXO3..] has an empty nsites slot, using 100.
Note: motif [FOXO4..] has an empty nsites slot, using 100.
Note: motif [FOXP2..] has an empty nsites slot, using 100.
Note: motif [FOXQ1..] has an empty nsites slot, using 100.
Note: motif [GABPA..] has an empty nsites slot, using 100.
Note: motif [GATA1..] has an empty nsites slot, using 100.
Note: motif [GATA2..] has an empty nsites slot, using 100.
Note: motif [GATA3..] has an empty nsites slot, using 100.
Note: motif [GATA4..] has an empty nsites slot, using 100.
Note: motif [GATA6..] has an empty nsites slot, using 100.
Note: motif [GCR_M..] has an empty nsites slot, using 100.
Note: motif [GFI1B..] has an empty nsites slot, using 100.
Note: motif [GFI1_..] has an empty nsites slot, using 100.
Note: motif [GLI1_..] has an empty nsites slot, using 100.
Note: motif [GRHL2..] has an empty nsites slot, using 100.
Note: motif [HAND1..] has an empty nsites slot, using 100.
Note: motif [HEN1_..] has an empty nsites slot, using 100.
Note: motif [HIC1_..] has an empty nsites slot, using 100.
Note: motif [HIF1A..] has an empty nsites slot, using 100.
Note: motif [HINFP..] has an empty nsites slot, using 100.
Note: motif [HLF_M..] has an empty nsites slot, using 100.
Note: motif [HNF1A..] has an empty nsites slot, using 100.
Note: motif [HNF1B..] has an empty nsites slot, using 100.
Note: motif [HNF4A..] has an empty nsites slot, using 100.
Note: motif [HNF4G..] has an empty nsites slot, using 100.
Note: motif [HNF6_..] has an empty nsites slot, using 100.
Note: motif [HSF1_..] has an empty nsites slot, using 100.
Note: motif [HSF2_..] has an empty nsites slot, using 100.
Note: motif [HTF4_..] has an empty nsites slot, using 100.
Note: motif [HXA10..] has an empty nsites slot, using 100.
Note: motif [HXA13..] has an empty nsites slot, using 100.
Note: motif [HXA1_..] has an empty nsites slot, using 100.
Note: motif [HXA9_..] has an empty nsites slot, using 100.
Note: motif [HXB4_..] has an empty nsites slot, using 100.
Note: motif [HXB7_..] has an empty nsites slot, using 100.
Note: motif [HXB8_..] has an empty nsites slot, using 100.
Note: motif [HXC9_..] has an empty nsites slot, using 100.
Note: motif [IKZF1..] has an empty nsites slot, using 100.
Note: motif [INSM1..] has an empty nsites slot, using 100.
Note: motif [IRF1_..] has an empty nsites slot, using 100.
Note: motif [IRF2_..] has an empty nsites slot, using 100.
Note: motif [IRF3_..] has an empty nsites slot, using 100.
Note: motif [IRF4_..] has an empty nsites slot, using 100.
Note: motif [IRF7_..] has an empty nsites slot, using 100.
Note: motif [IRF8_..] has an empty nsites slot, using 100.
Note: motif [IRF9_..] has an empty nsites slot, using 100.
Note: motif [ISL1_..] has an empty nsites slot, using 100.
Note: motif [ITF2_..] has an empty nsites slot, using 100.
Note: motif [JUNB_..] has an empty nsites slot, using 100.
Note: motif [JUND_..] has an empty nsites slot, using 100.
Note: motif [JUN_M..] has an empty nsites slot, using 100.
Note: motif [KAISO..] has an empty nsites slot, using 100.
Note: motif [KLF15..] has an empty nsites slot, using 100.
Note: motif [KLF1_..] has an empty nsites slot, using 100.
Note: motif [KLF3_..] has an empty nsites slot, using 100.
Note: motif [KLF4_..] has an empty nsites slot, using 100.
Note: motif [KLF5_..] has an empty nsites slot, using 100.
Note: motif [KLF6_..] has an empty nsites slot, using 100.
Note: motif [KLF8_..] has an empty nsites slot, using 100.
Note: motif [LEF1_..] has an empty nsites slot, using 100.
Note: motif [LHX2_..] has an empty nsites slot, using 100.
Note: motif [LHX3_..] has an empty nsites slot, using 100.
Note: motif [LHX6_..] has an empty nsites slot, using 100.
Note: motif [LYL1_..] has an empty nsites slot, using 100.
Note: motif [MAFB_..] has an empty nsites slot, using 100.
Note: motif [MAFF_..] has an empty nsites slot, using 100.
Note: motif [MAFG_..] has an empty nsites slot, using 100.
Note: motif [MAFK_..] has an empty nsites slot, using 100.
Note: motif [MAF_M..] has an empty nsites slot, using 100.
Note: motif [MAX_M..] has an empty nsites slot, using 100.
Note: motif [MAZ_M..] has an empty nsites slot, using 100.
Note: motif [MBD2_..] has an empty nsites slot, using 100.
Note: motif [MECP2..] has an empty nsites slot, using 100.
Note: motif [MEF2A..] has an empty nsites slot, using 100.
Note: motif [MEF2C..] has an empty nsites slot, using 100.
Note: motif [MEF2D..] has an empty nsites slot, using 100.
Note: motif [MEIS1..] has an empty nsites slot, using 100.
Note: motif [MEIS2..] has an empty nsites slot, using 100.
Note: motif [MITF_..] has an empty nsites slot, using 100.
Note: motif [MSGN1..] has an empty nsites slot, using 100.
Note: motif [MTF1_..] has an empty nsites slot, using 100.
Note: motif [MXI1_..] has an empty nsites slot, using 100.
Note: motif [MYBA_..] has an empty nsites slot, using 100.
Note: motif [MYB_M..] has an empty nsites slot, using 100.
Note: motif [MYCN_..] has an empty nsites slot, using 100.
Note: motif [MYC_M..] has an empty nsites slot, using 100.
Note: motif [MYF6_..] has an empty nsites slot, using 100.
Note: motif [MYOD1..] has an empty nsites slot, using 100.
Note: motif [MYOG_..] has an empty nsites slot, using 100.
Note: motif [NANOG..] has an empty nsites slot, using 100.
Note: motif [NDF1_..] has an empty nsites slot, using 100.
Note: motif [NDF2_..] has an empty nsites slot, using 100.
Note: motif [NF2L1..] has an empty nsites slot, using 100.
Note: motif [NF2L2..] has an empty nsites slot, using 100.
Note: motif [NFAC1..] has an empty nsites slot, using 100.
Note: motif [NFAC2..] has an empty nsites slot, using 100.
Note: motif [NFAC3..] has an empty nsites slot, using 100.
Note: motif [NFAC4..] has an empty nsites slot, using 100.
Note: motif [NFE2_..] has an empty nsites slot, using 100.
Note: motif [NFIA_..] has an empty nsites slot, using 100.
Note: motif [NFIB_..] has an empty nsites slot, using 100.
Note: motif [NFIC_..] has an empty nsites slot, using 100.
Note: motif [NFIL3..] has an empty nsites slot, using 100.
Note: motif [NFKB1..] has an empty nsites slot, using 100.
Note: motif [NFKB2..] has an empty nsites slot, using 100.
Note: motif [NFYA_..] has an empty nsites slot, using 100.
Note: motif [NFYB_..] has an empty nsites slot, using 100.
Note: motif [NFYC_..] has an empty nsites slot, using 100.
Note: motif [NGN2_..] has an empty nsites slot, using 100.
Note: motif [NKX21..] has an empty nsites slot, using 100.
Note: motif [NKX22..] has an empty nsites slot, using 100.
Note: motif [NKX25..] has an empty nsites slot, using 100.
Note: motif [NKX28..] has an empty nsites slot, using 100.
Note: motif [NKX31..] has an empty nsites slot, using 100.
Note: motif [NKX32..] has an empty nsites slot, using 100.
Note: motif [NKX61..] has an empty nsites slot, using 100.
Note: motif [NOBOX..] has an empty nsites slot, using 100.
Note: motif [NR1D1..] has an empty nsites slot, using 100.
Note: motif [NR1D2..] has an empty nsites slot, using 100.
Note: motif [NR1H3..] has an empty nsites slot, using 100.
Note: motif [NR1H4..] has an empty nsites slot, using 100.
Note: motif [NR1I2..] has an empty nsites slot, using 100.
Note: motif [NR1I3..] has an empty nsites slot, using 100.
Note: motif [NR2C1..] has an empty nsites slot, using 100.
Note: motif [NR2C2..] has an empty nsites slot, using 100.
Note: motif [NR2E3..] has an empty nsites slot, using 100.
Note: motif [NR4A1..] has an empty nsites slot, using 100.
Note: motif [NR4A2..] has an empty nsites slot, using 100.
Note: motif [NR5A2..] has an empty nsites slot, using 100.
Note: motif [NRF1_..] has an empty nsites slot, using 100.
Note: motif [OLIG2..] has an empty nsites slot, using 100.
Note: motif [OTX2_..] has an empty nsites slot, using 100.
Note: motif [OVOL1..] has an empty nsites slot, using 100.
Note: motif [OVOL2..] has an empty nsites slot, using 100.
Note: motif [P53_M..] has an empty nsites slot, using 100.
Note: motif [P63_M..] has an empty nsites slot, using 100.
Note: motif [P73_M..] has an empty nsites slot, using 100.
Note: motif [PAX5_..] has an empty nsites slot, using 100.
Note: motif [PAX6_..] has an empty nsites slot, using 100.
Note: motif [PBX1_..] has an empty nsites slot, using 100.
Note: motif [PBX2_..] has an empty nsites slot, using 100.
Note: motif [PBX3_..] has an empty nsites slot, using 100.
Note: motif [PDX1_..] has an empty nsites slot, using 100.
Note: motif [PEBB_..] has an empty nsites slot, using 100.
Note: motif [PIT1_..] has an empty nsites slot, using 100.
Note: motif [PITX1..] has an empty nsites slot, using 100.
Note: motif [PKNX1..] has an empty nsites slot, using 100.
Note: motif [PO2F1..] has an empty nsites slot, using 100.
Note: motif [PO2F2..] has an empty nsites slot, using 100.
Note: motif [PO3F1..] has an empty nsites slot, using 100.
Note: motif [PO3F2..] has an empty nsites slot, using 100.
Note: motif [PO5F1..] has an empty nsites slot, using 100.
Note: motif [PPARA..] has an empty nsites slot, using 100.
Note: motif [PPARG..] has an empty nsites slot, using 100.
Note: motif [PRD14..] has an empty nsites slot, using 100.
Note: motif [PRD16..] has an empty nsites slot, using 100.
Note: motif [PRDM1..] has an empty nsites slot, using 100.
Note: motif [PRDM5..] has an empty nsites slot, using 100.
Note: motif [PRDM9..] has an empty nsites slot, using 100.
Note: motif [PRGR_..] has an empty nsites slot, using 100.
Note: motif [PROP1..] has an empty nsites slot, using 100.
Note: motif [PRRX2..] has an empty nsites slot, using 100.
Note: motif [PTF1A..] has an empty nsites slot, using 100.
Note: motif [RARA_..] has an empty nsites slot, using 100.
Note: motif [RARG_..] has an empty nsites slot, using 100.
Note: motif [RELB_..] has an empty nsites slot, using 100.
Note: motif [REL_M..] has an empty nsites slot, using 100.
Note: motif [REST_..] has an empty nsites slot, using 100.
Note: motif [RFX1_..] has an empty nsites slot, using 100.
Note: motif [RFX2_..] has an empty nsites slot, using 100.
Note: motif [RFX3_..] has an empty nsites slot, using 100.
Note: motif [RFX6_..] has an empty nsites slot, using 100.
Note: motif [RORA_..] has an empty nsites slot, using 100.
Note: motif [RORG_..] has an empty nsites slot, using 100.
Note: motif [RUNX1..] has an empty nsites slot, using 100.
Note: motif [RUNX2..] has an empty nsites slot, using 100.
Note: motif [RUNX3..] has an empty nsites slot, using 100.
Note: motif [RXRA_..] has an empty nsites slot, using 100.
Note: motif [RXRB_..] has an empty nsites slot, using 100.
Note: motif [RXRG_..] has an empty nsites slot, using 100.
Note: motif [SALL4..] has an empty nsites slot, using 100.
Note: motif [SIX2_..] has an empty nsites slot, using 100.
Note: motif [SIX4_..] has an empty nsites slot, using 100.
Note: motif [SMAD2..] has an empty nsites slot, using 100.
Note: motif [SMAD3..] has an empty nsites slot, using 100.
Note: motif [SMAD4..] has an empty nsites slot, using 100.
Note: motif [SMCA5..] has an empty nsites slot, using 100.
Note: motif [SNAI1..] has an empty nsites slot, using 100.
Note: motif [SNAI2..] has an empty nsites slot, using 100.
Note: motif [SOX10..] has an empty nsites slot, using 100.
Note: motif [SOX2_..] has an empty nsites slot, using 100.
Note: motif [SOX3_..] has an empty nsites slot, using 100.
Note: motif [SOX4_..] has an empty nsites slot, using 100.
Note: motif [SOX5_..] has an empty nsites slot, using 100.
Note: motif [SOX9_..] has an empty nsites slot, using 100.
Note: motif [SP1_M..] has an empty nsites slot, using 100.
Note: motif [SP2_M..] has an empty nsites slot, using 100.
Note: motif [SP3_M..] has an empty nsites slot, using 100.
Note: motif [SP4_M..] has an empty nsites slot, using 100.
Note: motif [SP5_M..] has an empty nsites slot, using 100.
Note: motif [SP7_M..] has an empty nsites slot, using 100.
Note: motif [SPI1_..] has an empty nsites slot, using 100.
Note: motif [SPIB_..] has an empty nsites slot, using 100.
Note: motif [SRBP1..] has an empty nsites slot, using 100.
Note: motif [SRBP2..] has an empty nsites slot, using 100.
Note: motif [SRF_M..] has an empty nsites slot, using 100.
Note: motif [SRY_M..] has an empty nsites slot, using 100.
Note: motif [STA5A..] has an empty nsites slot, using 100.
Note: motif [STA5B..] has an empty nsites slot, using 100.
Note: motif [STAT1..] has an empty nsites slot, using 100.
Note: motif [STAT2..] has an empty nsites slot, using 100.
Note: motif [STAT3..] has an empty nsites slot, using 100.
Note: motif [STAT4..] has an empty nsites slot, using 100.
Note: motif [STAT6..] has an empty nsites slot, using 100.
Note: motif [STF1_..] has an empty nsites slot, using 100.
Note: motif [SUH_M..] has an empty nsites slot, using 100.
Note: motif [TAF1_..] has an empty nsites slot, using 100.
Note: motif [TAL1_..] has an empty nsites slot, using 100.
Note: motif [TBP_M..] has an empty nsites slot, using 100.
Note: motif [TBX20..] has an empty nsites slot, using 100.
Note: motif [TBX21..] has an empty nsites slot, using 100.
Note: motif [TBX3_..] has an empty nsites slot, using 100.
Note: motif [TCF7_..] has an empty nsites slot, using 100.
Note: motif [TEAD1..] has an empty nsites slot, using 100.
Note: motif [TEAD2..] has an empty nsites slot, using 100.
Note: motif [TEAD4..] has an empty nsites slot, using 100.
Note: motif [TF2L1..] has an empty nsites slot, using 100.
Note: motif [TF65_..] has an empty nsites slot, using 100.
Note: motif [TF7L1..] has an empty nsites slot, using 100.
Note: motif [TF7L2..] has an empty nsites slot, using 100.
Note: motif [TFE2_..] has an empty nsites slot, using 100.
Note: motif [TFE3_..] has an empty nsites slot, using 100.
Note: motif [TFEB_..] has an empty nsites slot, using 100.
Note: motif [TGIF1..] has an empty nsites slot, using 100.
Note: motif [THA11..] has an empty nsites slot, using 100.
Note: motif [THA_M..] has an empty nsites slot, using 100.
Note: motif [TWST1..] has an empty nsites slot, using 100.
Note: motif [TYY1_..] has an empty nsites slot, using 100.
Note: motif [USF1_..] has an empty nsites slot, using 100.
Note: motif [USF2_..] has an empty nsites slot, using 100.
Note: motif [VDR_M..] has an empty nsites slot, using 100.
Note: motif [VSX2_..] has an empty nsites slot, using 100.
Note: motif [WT1_M..] has an empty nsites slot, using 100.
Note: motif [XBP1_..] has an empty nsites slot, using 100.
Note: motif [ZBT17..] has an empty nsites slot, using 100.
Note: motif [ZBT7A..] has an empty nsites slot, using 100.
Note: motif [ZEB1_..] has an empty nsites slot, using 100.
Note: motif [ZFP42..] has an empty nsites slot, using 100.
Note: motif [ZFP57..] has an empty nsites slot, using 100.
Note: motif [ZFX_M..] has an empty nsites slot, using 100.
Note: motif [ZIC1_..] has an empty nsites slot, using 100.
Note: motif [ZIC2_..] has an empty nsites slot, using 100.
Note: motif [ZIC3_..] has an empty nsites slot, using 100.
Note: motif [ZKSC1..] has an empty nsites slot, using 100.
Note: motif [ZN143..] has an empty nsites slot, using 100.
Note: motif [ZN281..] has an empty nsites slot, using 100.
Note: motif [ZN322..] has an empty nsites slot, using 100.
Note: motif [ZN335..] has an empty nsites slot, using 100.
Note: motif [ZN431..] has an empty nsites slot, using 100.
PWMs <- do.call(PWMatrixList,Hocomocov11)

DefaultAssay(s.data) <- "peaks"

# add motif information
s.data <- Signac::AddMotifs(
  object = s.data,
  genome = BSgenome.Mmusculus.UCSC.mm10,
  pfm = PWMs
)
Building motif matrix
Finding motif positions
Creating Motif object
DefaultAssay(s.data) <- "peaks"
closest.features <- ClosestFeature(s.data, regions = rownames(s.data))
saveRDS(closest.features, file="../analyses/E12R1_nmm_closest_features.271021.Rds")
s.data <- RunChromVAR(
  object = s.data,
  genome = BSgenome.Mmusculus.UCSC.mm10
)
Computing GC bias per region
Selecting background regions
Computing deviations from background
Constructing chromVAR assay
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')

Identification of markers for clusters defined based on both modalities

# We need to run detection separately for both modality and then combine via AUC score
  
print("Running presto::wilcoxauc for RNA modality")
[1] "Running presto::wilcoxauc for RNA modality"
DefaultAssay(s.data) <- "RNA"
markers_rna <- presto:::wilcoxauc.Seurat(X = s.data, group_by = "seurat_clusters", assay = 'data', seurat_assay = 'RNA')

print("Running presto::wilcoxauc for ATAC modality")
[1] "Running presto::wilcoxauc for ATAC modality"
DefaultAssay(s.data) <- "peaks"
markers_atac <- presto:::wilcoxauc.Seurat(X = s.data, group_by = "seurat_clusters", assay = 'data', seurat_assay = 'peaks')

markers.atac.annotated <- as_tibble(cbind(markers_atac, closest.features))
Warning in data.frame(..., check.names = FALSE) :
  row names were found from a short variable and have been discarded
# Then we need to 1) annotate ATAC features 2) combine with RNA modality 3) Think of its presentation

#saveRDS(atac.expression.markers, file = paste("../analyses/", sample.name,".atac.expression.markers.mm.Rds",sep=""))

Finding overrepresented motifs among markers_atac features

motif.markers <- markers.atac.annotated %>% filter(logFC > 0.25 & padj <= 0.01) %>% group_by(group) %>% select(feature, group) %>% group_modify(~FindMotifs(object=s.data, features=.x$feature)) %>% filter(pvalue <= 0.01 & fold.enrichment >= 1.5)
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1163 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1880 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 666 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 709 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 4543 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1980 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2459 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2252 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1063 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1309 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2916 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 437 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2067 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 4766 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2991 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 4077 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1788 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2006 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 3518 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 3443 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1852 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1162 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 4499 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2633 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1217 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1571 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1044 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 2907 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 805 regions
Selecting background regions to match input sequence characteristics
Matching GC.percent distribution
Testing motif enrichment in 1213 regions
DefaultAssay(s.data) <- "chromvar"
markers_chromvar <- as_tibble(FindAllMarkers(
  object = s.data,
  only.pos = TRUE,
  test.use = 'LR',
  latent.vars = 'nCount_peaks'
)) %>% filter(p_val_adj <= 0.01 & avg_log2FC >= 0.75)
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
Calculating cluster 7
Calculating cluster 8
Calculating cluster 9
Calculating cluster 10
Calculating cluster 11
Calculating cluster 12
Calculating cluster 13
Calculating cluster 14
Calculating cluster 15
Calculating cluster 16
Calculating cluster 17
Calculating cluster 18
Calculating cluster 19
Calculating cluster 20
Calculating cluster 21
Calculating cluster 22
Calculating cluster 23
Calculating cluster 24
Calculating cluster 25
Calculating cluster 26
Calculating cluster 27
Calculating cluster 28
Calculating cluster 29
Calculating cluster 30
# Now we need to combine markers_rna, markers_atac, markers_chromvar, motif.markers in meaningful output to help with cluster annotation

# Writing marker info out to be used separately, writing out top 100
top.markers_rna <- as_tibble(markers_rna) %>% dplyr::filter(padj <= 0.01) %>% group_by(group) %>% top_n(n = 25, wt = logFC)
top.markers_rna <- left_join(top.markers_rna, feature.metadata.rna, by=c("feature"="gene_id"))

top.markers_atac <- as_tibble(markers.atac.annotated) %>% dplyr::filter(padj <= 0.01) %>% group_by(group) %>% top_n(n = 25, wt = logFC)

top.markers_chromvar <- as_tibble(markers_chromvar) %>% dplyr::filter(p_val_adj <= 0.01) %>% group_by(cluster) %>% top_n(n = 25, wt = avg_log2FC)

top.markers_motifs <- as_tibble(motif.markers) %>% dplyr::filter(pvalue <= 0.01) %>% group_by(group) %>% top_n(n = 25, wt = fold.enrichment)

save(list=c("top.markers_rna","top.markers_atac","top.markers_chromvar","top.markers_motifs"), file=paste("../analyses/e12R1_nmm_scATAC_cluster_markers.",run.date,".RData",sep=""))

Saving data for downstream analyses

saveRDS(s.data,paste("../scATAC_data/",sample.name,"_DownstreamReady_nmm_.",run.date,".Rds",sep=""))
s.data.slim <- s.data
DefaultAssay(s.data.slim) <- "peaks"
s.data.slim[['peaks_count']] <- NULL
s.data.slim[['Activity']] <- NULL
saveRDS(s.data.slim,paste("../scATAC_data/",sample.name,"_DownstreamReady_nmm_slim.",run.date,".Rds",sep=""))
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Users/kilpinen/opt/anaconda3/envs/r411_291021/lib/libopenblasp-r0.3.18.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] splines   grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] universalmotif_1.12.4              learnMotifs_0.1.0                  cicero_1.12.0                     
 [4] Gviz_1.38.3                        monocle_2.22.0                     DDRTree_0.1.5                     
 [7] VGAM_1.1-5                         future_1.22.1                      motifmatchr_1.16.0                
[10] flexdashboard_0.5.2                ggplotify_0.1.0                    waffle_0.7.0                      
[13] plotly_4.10.0                      pheatmap_1.0.12                    org.Mm.eg.db_3.14.0               
[16] EnsDb.Mmusculus.v79_2.99.0         ensembldb_2.18.0                   AnnotationFilter_1.18.0           
[19] GenomicFeatures_1.46.1             AnnotationDbi_1.56.0               Biobase_2.54.0                    
[22] BSgenome.Mmusculus.UCSC.mm10_1.4.3 BSgenome_1.62.0                    rtracklayer_1.54.0                
[25] Biostrings_2.62.0                  XVector_0.34.0                     TFBSTools_1.32.0                  
[28] forcats_0.5.1                      stringr_1.4.0                      dplyr_1.0.7                       
[31] purrr_0.3.4                        readr_2.0.2                        tidyr_1.1.4                       
[34] tibble_3.1.5                       ggplot2_3.3.5                      tidyverse_1.3.1                   
[37] chromVAR_1.16.0                    SeuratWrappers_0.3.0               reldist_1.6-6                     
[40] regioneR_1.26.0                    SeuratObject_4.0.2                 Seurat_4.0.5                      
[43] Signac_1.4.0                       gridExtra_2.3                      fastcluster_1.2.3                 
[46] RColorBrewer_1.1-2                 clusterProfiler_4.2.0              umap_0.2.7.0                      
[49] irlba_2.3.3                        densityClust_0.3                   genomation_1.26.0                 
[52] Rtsne_0.15                         proxy_0.4-26                       Matrix_1.3-4                      
[55] gplots_3.1.1                       plyr_1.8.6                         data.table_1.14.2                 
[58] GenomicRanges_1.46.1               GenomeInfoDb_1.30.0                IRanges_2.28.0                    
[61] S4Vectors_0.32.0                   BiocGenerics_0.40.0                DT_0.19                           
[64] viridisLite_0.4.0                  pacman_0.5.1                      

loaded via a namespace (and not attached):
  [1] graphlayouts_0.7.1          pbapply_1.5-0               lattice_0.20-45            
  [4] haven_2.4.3                 vctrs_0.3.8                 fastICA_1.2-3              
  [7] mgcv_1.8-38                 blob_1.2.2                  survival_3.2-13            
 [10] spatstat.data_2.1-0         later_1.3.0                 DBI_1.1.1                  
 [13] R.utils_2.11.0              rappdirs_0.3.3              uwot_0.1.10                
 [16] jpeg_0.1-9                  tensorflow_2.8.0            zlibbioc_1.40.0            
 [19] htmlwidgets_1.5.4           leiden_0.3.9                parallel_4.1.1             
 [22] tidygraph_1.2.0             Rcpp_1.0.7                  KernSmooth_2.23-20         
 [25] promises_1.2.0.1            DelayedArray_0.20.0         limma_3.50.0               
 [28] Hmisc_4.6-0                 RSpectra_0.16-0             fs_1.5.0                   
 [31] fastmatch_1.1-3             presto_1.0.0                digest_0.6.28              
 [34] png_0.1-7                   qlcMatrix_0.9.7             sctransform_0.3.2          
 [37] scatterpie_0.1.7            cowplot_1.1.1               DOSE_3.20.0                
 [40] ggraph_2.0.5                pkgconfig_2.0.3             GO.db_3.14.0               
 [43] docopt_0.7.1                gridBase_0.4-7              reticulate_1.24            
 [46] SummarizedExperiment_1.24.0 xfun_0.27                   bslib_0.3.1                
 [49] zoo_1.8-9                   tidyselect_1.1.1            reshape2_1.4.4             
 [52] ica_1.0-2                   rlang_0.4.12                jquerylib_0.1.4            
 [55] glue_1.4.2                  modelr_0.1.8                CNEr_1.30.0                
 [58] matrixStats_0.61.0          MatrixGenerics_1.6.0        ggseqlogo_0.1              
 [61] labeling_0.4.2              httpuv_1.6.3                Rttf2pt1_1.3.9             
 [64] seqLogo_1.60.0              DO.db_2.9                   annotate_1.72.0            
 [67] jsonlite_1.7.2              bit_4.0.4                   mime_0.12                  
 [70] nabor_0.5.0                 Rsamtools_2.10.0            stringi_1.7.5              
 [73] RcppRoll_0.3.0              spatstat.sparse_2.0-0       scattermore_0.7            
 [76] yulab.utils_0.0.4           bitops_1.0-7                cli_3.1.0                  
 [79] RSQLite_2.2.8               rstudioapi_0.13             GenomicAlignments_1.30.0   
 [82] nlme_3.1-153                qvalue_2.26.0               VariantAnnotation_1.40.0   
 [85] listenv_0.8.0               SnowballC_0.7.0             miniUI_0.1.1.1             
 [88] gridGraphics_0.5-1          R.oo_1.24.0                 dbplyr_2.1.1               
 [91] readxl_1.3.1                lifecycle_1.0.1             munsell_0.5.0              
 [94] cellranger_1.1.0            R.methodsS3_1.8.1           caTools_1.18.2             
 [97] codetools_0.2-18            lmtest_0.9-38               htmlTable_2.3.0            
[100] xtable_1.8-4                ROCR_1.0-11                 BiocManager_1.30.16        
[103] abind_1.4-5                 farver_2.1.0                FNN_1.1.3                  
[106] parallelly_1.28.1           RANN_2.6.1                  aplot_0.1.1                
[109] askpass_1.1                 biovizBase_1.42.0           poweRlaw_0.70.6            
[112] sparsesvd_0.2               ggtree_3.2.0                BiocIO_1.4.0               
[115] keras_2.8.0                 RcppAnnoy_0.0.19            goftest_1.2-3              
[118] patchwork_1.1.1             dichromat_2.0-0             cluster_2.1.2              
[121] future.apply_1.8.1          zeallot_0.1.0               extrafontdb_1.0            
[124] tidytree_0.3.5              ellipsis_0.3.2              prettyunits_1.1.1          
[127] lubridate_1.8.0             ggridges_0.5.3              reprex_2.0.1               
[130] igraph_1.2.7                fgsea_1.20.0                remotes_2.4.1              
[133] slam_0.1-48                 seqPattern_1.26.0           spatstat.utils_2.2-0       
[136] htmltools_0.5.2             BiocFileCache_2.2.0         yaml_2.2.1                 
[139] utf8_1.2.2                  XML_3.99-0.8                foreign_0.8-81             
[142] withr_2.4.2                 fitdistrplus_1.1-6          BiocParallel_1.28.0        
[145] bit64_4.0.5                 ProtGenerics_1.26.0         spatstat.core_2.3-0        
[148] combinat_0.0-8              GOSemSim_2.20.0             rsvd_1.0.5                 
[151] memoise_2.0.0               evaluate_0.14               tzdb_0.2.0                 
[154] extrafont_0.17              curl_4.3.2                  fansi_0.5.0                
[157] tensor_1.5                  checkmate_2.0.0             cachem_1.0.6               
[160] deldir_1.0-6                impute_1.68.0               rjson_0.2.20               
[163] ggrepel_0.9.1               tools_4.1.1                 sass_0.4.0                 
[166] magrittr_2.0.1              RCurl_1.98-1.5              TFMPvalue_0.0.8            
[169] ape_5.5                     xml2_1.3.2                  httr_1.4.2                 
[172] assertthat_0.2.1            rmarkdown_2.11              globals_0.14.0             
[175] R6_2.5.1                    nnet_7.3-16                 DirichletMultinomial_1.36.0
[178] progress_1.2.2              KEGGREST_1.34.0             treeio_1.18.0              
[181] gtools_3.9.2                lsa_0.73.2                  ggfun_0.0.4                
[184] colorspace_2.0-2            generics_0.1.1              base64enc_0.1-3            
[187] pracma_2.3.3                pillar_1.6.4                tweenr_1.0.2               
[190] HSMMSingleCell_1.13.0       GenomeInfoDbData_1.2.7      gtable_0.3.0               
[193] rvest_1.0.2                 restfulr_0.0.13             knitr_1.36                 
[196] latticeExtra_0.6-29         shadowtext_0.0.9            biomaRt_2.50.0             
[199] fastmap_1.1.0               crosstalk_1.1.1             tfruns_1.5.0               
[202] broom_0.7.9                 openssl_1.4.5               scales_1.1.1               
[205] filelock_1.0.2              backports_1.3.0             plotrix_3.8-2              
[208] vroom_1.5.5                 enrichplot_1.14.0           hms_1.1.1                  
[211] ggforce_0.3.3               shiny_1.7.1                 polyclip_1.10-0            
[214] lazyeval_0.2.2              Formula_1.2-4               whisker_0.4                
[217] crayon_1.4.1                MASS_7.3-54                 downloader_0.4             
[220] viridis_0.6.2               rpart_4.1-15                compiler_4.1.1             
[223] spatstat.geom_2.3-0        

Tables

Cross-tabulation (confusion matrix) in to what extent each scRNA based cell type is included in each scATAC cluster.

scATAC.clusters <- Idents(s.data)
scRNA.clusters <- s.data@meta.data$predicted.id

conf.mat <- table(as.factor(scRNA.clusters),scATAC.clusters)
create_dt(as.data.frame.matrix(conf.mat))

Cross-tabulation (confusion matrix) in to what extent each scRNA based cell type is included in each scATAC cluster.

Table format of the top1 gene expression by scATAC cluster dotplot

# create_dt(as.data.frame.matrix(d.plot$data))

Top 20 marker genes per scATAC cluster

# top.20.markers.by.cluster <- e14di.atc.expression.markers %>% group_by(cluster) %>% top_n(n = 20, wt = avg_log2FC)
# create_dt(as.data.frame.matrix(data.frame(top.20.markers.by.cluster)))

Marker table filtered for convenience having top 20 genes pre cluster

scRNA cluster marker genes (top20 per cluster)

# clean_top20_markers <- read_tsv(scRNA_clean_markers_file)
# create_dt(clean_top20_markers)

---
title: "R Notebook of e12r1 downstream analysis from binarized peak level data with joint feature space"
output: html_notebook
---

```{r Load libraries, include=FALSE}
if (Sys.info()['sysname']=="Linux") {
    .libPaths(c("/projappl/project_2001539/project_rpackages", .libPaths()))
    libpath <- .libPaths()[1]
}

load.libs <- c(
  "viridisLite",
  "DT",
  "GenomicRanges",
  "data.table",
  "plyr",
  "gplots",
  "Matrix",
  "proxy",
  "Rtsne",
  "genomation",
  "densityClust",
  "irlba",
  "umap",
  "clusterProfiler",
  "RColorBrewer",
  "fastcluster",
  "gridExtra",
  "Signac",
  "Seurat",
  "regioneR",
  "reldist",
  "SeuratWrappers",
  "chromVAR",
  "tidyverse",
  "TFBSTools",
  "BSgenome.Mmusculus.UCSC.mm10",
  "EnsDb.Mmusculus.v79",
  "org.Mm.eg.db",
  "pheatmap",
  "plotly",
  "waffle",
  "ggplotify",
  "flexdashboard",
  "motifmatchr",
  "future",
  "cicero",
  "universalmotif")
if (!require("pacman")) install.packages("pacman"); library(pacman)
p_load(load.libs, update = FALSE, character.only = TRUE)
status <- sapply(load.libs,require,character.only = TRUE)
if(all(status)){
    print("SUCCESS: You have successfully installed and loaded all required libraries.")
} else{
    cat("ERROR: One or more libraries failed to install correctly. Check the following list for FALSE cases and try again...\n\n")
    status
}

set.seed(2020);
```
About 
===================================== 
Process from counts matrix to ready for downstream analysis

In this phase we do final filtering, binarization, TF-IDF, SVD, UMAP for scATAC data based on binarized count over peaks identified over clades in phases 1-3, extract transcript expression signal from scATAC data, plot marker genes, find tentative cluster ids, integrate with scRNA etc...


```{r Aux functions and number of cores, include=FALSE}
if (Sys.info()['sysname']=="Darwin") {
    source("../../generic code/AuxFunctions.R")
    cores=3
} else {
    source("~/Research/⁨GitHub/⁨scATAC-pipelines⁩/AuxFunctions.R")
    cores=10
}
```

```{r Setting Seurat multicore}
plan("multisession", workers = 6)
options(future.globals.maxSize = 12 * 1024 ^ 3)
```

```{r Setwd sample name specific variables and read data objects, message=FALSE}
#setwd("/Volumes/ExtSSD/ExtraWorkspace/E12R1/scATAC_data/")
sample.name <- "E12_R1"
run.date <- "240322"
# Reading scRNA data rds object
#s.data_rna <- readRDS("../scRNA/E14_DI_scRNAseq_neurons_clean.rds")
#cluster_names<-read_tsv("../scRNA/E14_DI_scRNAseq_cleaned_top20_allclusters.csv")
#cluster_id_name <- distinct(cluster_names[,c("cluster","ClusterName")])->cluster_id_name
#scRNA_clean_markers_file <- "../scRNA/E14_DI_scRNAseq_cleaned_top20_allclusters.csv"
```

```{r Reading in data objects}
s.data <- readRDS(paste("../scATAC_data/",sample.name,".merged.peaks.271021.Rds",sep=""))
s.data_RNA <- readRDS("../scRNA_data/e12_ens_whole_rescaled.Rds")
s.data_RNA$clusterAnnotation <- s.data_RNA$seurat_clusters
feature.metadata.rna <- s.data_RNA[['RNA']][[]] %>% rownames_to_column("gene_id") %>% as_tibble()
```


```{r TFIDF and SVD, cache=TRUE, message=FALSE}
DefaultAssay(s.data) <- 'peaks'

set.seed(2020)

s.data <- RunTFIDF(s.data, method=3)
s.data <- FindTopFeatures(s.data, min.cutoff = 'q25')
s.data <- RunSVD(
  object = s.data,
  assay = 'peaks',
  reduction.key = 'LSI_',
  reduction.name = 'lsi'
)
```


Visualizations {data-icon="fa-signal"}
=====================================

### Read depth and dimension reduction component correlation

```{r plot_depth_dimension_corr}
DepthCor(s.data)
```

---

Plotting read depth correlation plot, in Seurat pipeline usually first PC1 is omitted because of this association.

### scATAC UMAP clusters

```{r UMAP and cluster detection, cache=TRUE}
s.data <- RunUMAP(object = s.data, reduction = 'lsi', dims = 2:30, min.dist=0.05, spread=1.1)
s.data <- FindNeighbors(object = s.data, reduction = 'lsi', dims = 2:30)
s.data <- FindClusters(object = s.data, verbose = FALSE, algorithm = 4, resolution = 1)
DimPlot(object = s.data, label = TRUE, pt.size=1.2) + NoLegend()
```

```{r UMAP with biological replicates colored}
DimPlot(object = s.data, label = TRUE, pt.size=.5,group.by="replicate", shuffle =TRUE) + NoLegend()
```

```{r Create a gene activity matrix}
# Compute gene activities
# Now that scRNA data has been processed with ENSMUSG ids this now longer works directly like this.
# Let's extract genomic annotation metadata from s.data[['peaks']]
genomic.metadata <- mcols(Annotation(s.data[['peaks']]))
# Generate conversion table from gene_name to gene_id
gene_name2gene_id <- as_tibble(genomic.metadata[,c("gene_name","gene_id")])

# Calculate gene activity estimate from scATAC reads based on the scATAC, by using gene_names as function does not support any other id
gene.activities <- GeneActivity(s.data, assay="peaks")

# Store gene_names
gene.names <- rownames(gene.activities)

# Switch sparse matrix to use ensmusg id
ensmusg.ids <- gene_name2gene_id[match(gene.names,pull(gene_name2gene_id,"gene_name")),] %>% pull("gene_id")
gene_names <- gene_name2gene_id[match(gene.names,pull(gene_name2gene_id,"gene_name")),] %>% pull("gene_name")

# Dropping NAs
non.na.i <- !is.na(ensmusg.ids)
gene.activities.gene_id <- gene.activities[non.na.i,]
rownames(gene.activities.gene_id) <- ensmusg.ids[non.na.i]

# Add the gene activity matrix to the Seurat object as a new assay
s.data[['Activity']] <- CreateAssayObject(counts = gene.activities.gene_id)
s.data <- NormalizeData(
  object = s.data,
  assay = 'Activity',
  normalization.method = 'LogNormalize',
  scale.factor = median(s.data$nCount_Activity)
)

# Add gene_name to gene_id mapping into the s.data[['Activity']] assays metadata
s.data[['Activity']]<- AddMetaData(s.data[['Activity']], col.name = "feature_symbol", metadata = gene_names[non.na.i])
```

### scATAC clades projection into UMAP clusters

```{r Testing how clades from phase 1 situate in UMAP projection}
#DimPlot(object = s.data, label = TRUE, pt.size=1.2,group.by="clade") + NoLegend()
```

### Read marker gene list and do FeaturePlot

```{r Read marker gene list and do FeaturePlot, fig.height=20, fig.width=20, message=FALSE}
neuronal.markers<- read_tsv("../../CellAnnotation/E12.5_cluster_markers_for_ATACseq.txt", col_names = c("annotation","geneName"))
feature.metadata <- s.data[['Activity']][[]] %>% rownames_to_column(var="gene_id") %>% as_tibble()
neuronal.markers.tmp <- filter(feature.metadata, feature_symbol %in% neuronal.markers$geneName)

neuronal.markers.ids <- pull(neuronal.markers.tmp,"gene_id")
neuronal.markers.names <- pull(neuronal.markers.tmp,"feature_symbol")

DefaultAssay(s.data) <- 'Activity'

f.plot.tmp <- FeaturePlot(
  object = s.data,
  features = neuronal.markers.ids,
  pt.size = 0.1,
  max.cutoff = 'q95',
  combine = F
)

f.plots.1 <- lapply(1:length(f.plot.tmp),function(i){
  f.plot.tmp[[i]] + labs(title=neuronal.markers.names[i])
})

patchwork::wrap_plots(f.plots.1)
```

----

Plot of some marker genes with signal from 2kbp upstream and only from those feature 
regions (peaks) that form clusters seen in UMAP


### Perform integration with RNA sample

```{r Adding E12R1 cluster information from Morello et al.}
# morello_e12_clusters <- read_tsv("../scRNA_data/Morello_et_al_cluster_anno.tsv")
# 
# morello.i <- match(as.vector(s.data_RNA@meta.data$seurat_clusters),morello_e12_clusters$clusterNumber)
# 
# s.data_RNA <- AddMetaData(s.data_RNA, pull(morello_e12_clusters[morello.i,2], clusterName), col.name = "clusterAnnotation")
```

```{r Perform label transfer as per Satija et al, cache=TRUE}
# Converting numerical Seurat clusters to alphabeticals and store in $CellType slot of s.data_rna

# Finding transfer anchors
transfer.anchors <- FindTransferAnchors(
    reference = s.data_RNA,
    query = s.data,
    reduction = 'cca',
    reference.assay="RNA",
    query.assay = "Activity",
    features = VariableFeatures(object=s.data_RNA)
)

# TODO: Find out which object and why there are graphs(4?) without associated assays, this however based on googling doesn't show as meaningful warning()

predicted.labels <- TransferData(
  anchorset = transfer.anchors,
  refdata = s.data_RNA@meta.data$clusterAnnotation,
  weight.reduction = s.data[['lsi']],
  dims = 2:30
)

s.data <- AddMetaData(object = s.data, metadata = predicted.labels)
```

----

Label transfer, following and adapting https://satijalab.org/seurat/v3.0/atacseq_integration_vignette.html

### Plotting prediction scores

```{r Plot prediction scores, echo=FALSE}
pred.score.df <- data.frame(pred.score=s.data$prediction.score.max)
ggplot(pred.score.df, aes(x=pred.score)) + geom_histogram(binwidth=.025) + geom_vline(data=pred.score.df, aes(xintercept=0.5, color="red"),linetype="dashed") + theme(legend.position = 'none')
```

---

Histogram of prediction scores, in Satija lab integration process each cell get prediction score how well it can be mapped from dataspace to another. Values > 0.5 are considered to be "good".

### Fraction of cells mapping acceptable between scATAC and scRNA data spaces

```{r Calculate accepted prediction score fraction}
prediction.score.over.th <- table(s.data$prediction.score.max > 0.5)
p.freq <- prediction.score.over.th['TRUE']/(prediction.score.over.th['TRUE']+prediction.score.over.th['FALSE'])

p.score.th <- as.numeric(prediction.score.over.th)
val_names <- sprintf("%s (%s)", c("Match not found", "Match found"), scales::percent(round(p.score.th/sum(p.score.th), 2)))
names(p.score.th) <- val_names
waffle::waffle(p.score.th/10,colors = c("#fb8072", "#8dd3c7", "white"), rows=9, size=1)
```

---

Waffle plot visualizing proportions of properly mapping cells

### Label transfer visualization

```{r Select cells with accepted prediction score and plot results of label transfer, fig.height=10, fig.width=18, cache=TRUE}
#' Select only cells with prediction score over 0.5
s.data.filtered <- subset(s.data, subset = prediction.score.max > 0.5)

#' To make the colors match, TODO: Check why there are few NAs in the predicted ids
#s.data.filtered$predicted.id <- factor(s.data.filtered$predicted.id, levels = letters[as.numeric(levels(s.data_rna))]) 

# Do combined plotting
p1 <- DimPlot(s.data.filtered, group.by = "predicted.id", label = TRUE, repel = TRUE, label.size=7, pt.size=2) + NoLegend() + scale_colour_hue(drop = FALSE)
p2 <- DimPlot(s.data_RNA, group.by = "clusterAnnotation", label = TRUE, repel = TRUE, label.size=7) + NoLegend()

p1 + ggtitle("scATAC-seq cells, labels predicted from scRNA")
p2 + ggtitle("scRNA-seq cells")
```

---

Filtering for cells mapping properly and visualizing cluster labels from scRNA (right side) to scATAC (left side).

### Perform RNA data imputation into scATAC cells

```{r Perform scRNA data imputation, message=FALSE, include = FALSE}
#genes.use <- VariableFeatures(s.data_rna)
refdata <- GetAssayData(s.data_RNA, assay = "RNA", slot = "data")#[genes.use, ]

s.data_RNA@meta.data$tech<-"scRNA"
s.data@meta.data$tech<-"scATAC"

# refdata (input) contains a scRNA-seq expression matrix for the scRNA-seq cells.  
#' Imputation (output) will contain an imputed scRNA-seq matrix for each of the ATAC cells
imputation <- TransferData(anchorset = transfer.anchors, refdata = refdata, weight.reduction = s.data[["lsi"]], dims = 2:30)

# this line adds the imputed data matrix to the pbmc.atac object
s.data[["RNA"]] <- imputation
coembed <- merge(x = s.data_RNA, y = s.data)

# Copy feature metadata from s.data_rna to s.data
s.data_rna.feature.metadata <- s.data_RNA[["RNA"]][[]]
s.data[["RNA"]] <- AddMetaData(s.data[["RNA"]], metadata = s.data_rna.feature.metadata[rownames(s.data[["RNA"]]),"feature_symbol"], col.name = "feature_symbol")

# Find variable features
coembed <- FindVariableFeatures(coembed)

# Finally, we run PCA and UMAP on this combined object, to visualize the co-embedding of both
# datasets
coembed <- ScaleData(coembed, do.scale = FALSE)
coembed <- RunPCA(coembed, verbose = FALSE)
coembed <- RunUMAP(coembed, dims = 2:30)
coembed@meta.data$clusterAnnotation <- ifelse(!is.na(coembed@meta.data$clusterAnnotation), coembed@meta.data$clusterAnnotation, coembed@meta.data$predicted.id)
```

    Finding integration vectors
    
    Finding integration vector weights
    
    Transfering 27999 features onto reference data
    
    Centering data matrix
    
    11:43:23 UMAP embedding parameters a = 0.9922 b = 1.112
    
    11:43:23 Read 10354 rows and found 29 numeric columns
    
    11:43:23 Using Annoy for neighbor search, n_neighbors = 30
    
    11:43:23 Building Annoy index with metric = cosine, n_trees = 50
    
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    11:43:28 Writing NN index file to temp file /var/folders/yb/wvk8t07s719136klxnc8nmp9sglf93/T//Rtmpnq8Zdj/file72653e114263
    
    11:43:28 Searching Annoy index using 1 thread, search_k = 3000
    
    11:43:32 Annoy recall = 100%
    
    11:43:32 Commencing smooth kNN distance calibration using 1 thread
    
    11:43:35 Initializing from normalized Laplacian + noise
    
    11:43:35 Commencing optimization for 200 epochs, with 432826 positive edges
    
    11:43:45 Optimization finished
    



### Coembed dataset plotting

```{r Plot coembed data based on technology and cell type, cache=TRUE}
p1 <- DimPlot(coembed, group.by = "tech")
p2 <- DimPlot(coembed, group.by = "clusterAnnotation", label = TRUE, repel = TRUE)  + theme(legend.position="none") 

p1 + p2
```

---

Coembed plotting scATAC and scRNA cells together. Usable mainly for validation purposes.

```{r Filtering Seurat data object based on prediction score for all downstream analysis}
s.data <- subset(s.data, subset = prediction.score.max > 0.5)
```

```{r Regenerate UMAP after filtering based predictions score}
DefaultAssay(s.data) <- "peaks"
s.data <- RunUMAP(object = s.data, reduction = 'lsi', dims = 2:30, spread=1.4)
s.data <- FindNeighbors(object = s.data, reduction = 'lsi', dims = 2:30)
s.data <- FindClusters(object = s.data, verbose = FALSE, algorithm = 4, resolution = 1)
DimPlot(object = s.data, label = TRUE, pt.size=1.2) + NoLegend()
```


### Plot marker gene FeaturePlots with imputed scRNA data

```{r Plot marker gene FeaturePlots with imputed scRNA data, fig.height=20, fig.width=20, cache=TRUE, message=FALSE}
neuronal.markers<- read_tsv("../../CellAnnotation/E12.5_cluster_markers_for_ATACseq.txt", col_names = c("annotation","geneName"))
feature.metadata <- s.data[['Activity']][[]] %>% rownames_to_column(var="gene_id") %>% as_tibble()
neuronal.markers.tmp <- filter(feature.metadata, feature_symbol %in% neuronal.markers$geneName)

neuronal.markers.ids <- pull(neuronal.markers.tmp,"gene_id")
neuronal.markers.names <- pull(neuronal.markers.tmp,"feature_symbol")
DefaultAssay(s.data) <- 'RNA'

f.plot.tmp <- FeaturePlot(
  object = s.data,
  features = neuronal.markers.ids,
  pt.size = 0.1,
  max.cutoff = 'q95',
  combine = F
)

f.plots.2 <- lapply(1:length(f.plot.tmp),function(i){
  f.plot.tmp[[i]] + labs(title=neuronal.markers.names[i])
})

patchwork::wrap_plots(f.plots.2)
```

---

Plot marker gene FeaturePlots with imputed scRNA data

### Plot gini index based validation of clustering effectiveness

```{r Plot gini index based validation of clustering effectiveness, message=FALSE}
# Define a set of HK genes
hk.genes <- c("RRN18S","Actb","Gapdh","Pgk1","Ppia","Rpl13a","Rplp0","Arbp","B2M","Ywhaz","Sdha","Tfrc","Gusb","Hmbs","Hprt1","Tbp")

hk.genes.id <- convert_feature_identity(s.data, "RNA",features = hk.genes)
neuronal.markers.id <- convert_feature_identity(s.data, "RNA",features = neuronal.markers$geneName)

# Neuronal marker mean per cluster in Cusanovich data
gene.i <- match(neuronal.markers.id,s.data[['RNA']]@data@Dimnames[[1]])
gene.i<-gene.i[!is.na(gene.i)]
barcode.clusters <- s.data@meta.data$seurat_clusters
marker.matrix <- s.data[['RNA']]@data[gene.i,]
marker.tb <- as_tibble(t(as.data.frame(marker.matrix)))
marker.tb<-tibble(marker.tb,cluster=barcode.clusters)
marker.mean <- list()
marker.mean$mean.by.cluster <- marker.tb %>% group_by(cluster) %>% summarize_all(mean)

# HK gene mean per cluster in Cusanovich data
gene.i <- match(hk.genes.id,s.data[['RNA']]@data@Dimnames[[1]])
gene.i<-gene.i[!is.na(gene.i)]
barcode.clusters <- s.data@meta.data$seurat_clusters
marker.matrix <- s.data[['RNA']]@data[gene.i,]
marker.tb <- as_tibble(t(as.data.frame(marker.matrix)))
marker.tb<-tibble(marker.tb,cluster=barcode.clusters)
hk.mean <- list()
hk.mean$mean.by.cluster <- marker.tb %>% group_by(cluster) %>% summarize_all(mean)

# Gini indeces for Cusanovich data
cus.hk.gini <- apply(hk.mean$mean.by.cluster[,-1],2,gini)
cus.neur.gini <- apply(marker.mean$mean.by.cluster[,-1],2,gini)

gini.tb<-tibble(gini.index=c(cus.hk.gini,cus.neur.gini),type=c(rep("hk",length(cus.hk.gini)),rep("neur",length(cus.neur.gini)))) %>%  dplyr::filter(!is.na(gini.index))

ggplot(gini.tb, aes(x=gini.index,y=type,fill="blue"))+geom_boxplot(fill="lightblue")+ theme(legend.position="none") + theme_classic() 
```

---

Gini index based validation of clustering. 

### Adding Motif information into the object

```{r Adding Motif information to the object}
Hocomocov11 <- read_jaspar("../../mm10/HOCOMOCOv11_core_MOUSE_mono_jaspar_format.txt")
names(Hocomocov11) <- lapply(Hocomocov11,function(x){x@name})
Hocomocov11 <- convert_motifs(Hocomocov11, "TFBSTools-PWMatrix")
PWMs <- do.call(PWMatrixList,Hocomocov11)

DefaultAssay(s.data) <- "peaks"

# add motif information
s.data <- Signac::AddMotifs(
  object = s.data,
  genome = BSgenome.Mmusculus.UCSC.mm10,
  pfm = PWMs
)
```

```{r Finding closest features}
DefaultAssay(s.data) <- "peaks"
closest.features <- ClosestFeature(s.data, regions = rownames(s.data))
saveRDS(closest.features, file="../analyses/E12R1_nmm_closest_features.271021.Rds")
```

```{r Run ChromVar}
s.data <- RunChromVAR(
  object = s.data,
  genome = BSgenome.Mmusculus.UCSC.mm10
)
```

### Identification of markers for clusters defined based on both modalities

```{r Identification of cluster markers, message=FALSE}
# We need to run detection separately for both modality and then combine via AUC score
  
print("Running presto::wilcoxauc for RNA modality")
DefaultAssay(s.data) <- "RNA"
markers_rna <- presto:::wilcoxauc.Seurat(X = s.data, group_by = "seurat_clusters", assay = 'data', seurat_assay = 'RNA')

print("Running presto::wilcoxauc for ATAC modality")
DefaultAssay(s.data) <- "peaks"
markers_atac <- presto:::wilcoxauc.Seurat(X = s.data, group_by = "seurat_clusters", assay = 'data', seurat_assay = 'peaks')

markers.atac.annotated <- as_tibble(cbind(markers_atac, closest.features))
# Then we need to 1) annotate ATAC features 2) combine with RNA modality 3) Think of its presentation

#saveRDS(atac.expression.markers, file = paste("../analyses/", sample.name,".atac.expression.markers.mm.Rds",sep=""))
```

### Finding overrepresented motifs among markers_atac features

```{r Finding overrepresented motifs among markers_atac features, message=FALSE}
motif.markers <- markers.atac.annotated %>% filter(logFC > 0.25 & padj <= 0.01) %>% group_by(group) %>% select(feature, group) %>% group_modify(~FindMotifs(object=s.data, features=.x$feature)) %>% filter(pvalue <= 0.01 & fold.enrichment >= 1.5)
```

```{r Find markers for clusters based on chromvar, message=FALSE}
DefaultAssay(s.data) <- "chromvar"
markers_chromvar <- as_tibble(FindAllMarkers(
  object = s.data,
  only.pos = TRUE,
  test.use = 'LR',
  latent.vars = 'nCount_peaks'
)) %>% filter(p_val_adj <= 0.01 & avg_log2FC >= 0.75)
```

```{r Filter marker data to be saved}
# Now we need to combine markers_rna, markers_atac, markers_chromvar, motif.markers in meaningful output to help with cluster annotation

# Writing marker info out to be used separately, writing out top 100
top.markers_rna <- as_tibble(markers_rna) %>% dplyr::filter(padj <= 0.01) %>% group_by(group) %>% top_n(n = 25, wt = logFC)
top.markers_rna <- left_join(top.markers_rna, feature.metadata.rna, by=c("feature"="gene_id"))

top.markers_atac <- as_tibble(markers.atac.annotated) %>% dplyr::filter(padj <= 0.01) %>% group_by(group) %>% top_n(n = 25, wt = logFC)

top.markers_chromvar <- as_tibble(markers_chromvar) %>% dplyr::filter(p_val_adj <= 0.01) %>% group_by(cluster) %>% top_n(n = 25, wt = avg_log2FC)

top.markers_motifs <- as_tibble(motif.markers) %>% dplyr::filter(pvalue <= 0.01) %>% group_by(group) %>% top_n(n = 25, wt = fold.enrichment)

save(list=c("top.markers_rna","top.markers_atac","top.markers_chromvar","top.markers_motifs"), file=paste("../analyses/e12R1_nmm_scATAC_cluster_markers.",run.date,".RData",sep=""))
```

### Saving data for downstream analyses

```{r Saving data for downstream analyses}
saveRDS(s.data,paste("../scATAC_data/",sample.name,"_DownstreamReady_nmm_.",run.date,".Rds",sep=""))
```

```{r Saving data for downstream analyses slim}
s.data.slim <- s.data
DefaultAssay(s.data.slim) <- "peaks"
s.data.slim[['peaks_count']] <- NULL
s.data.slim[['Activity']] <- NULL
saveRDS(s.data.slim,paste("../scATAC_data/",sample.name,"_DownstreamReady_nmm_slim.",run.date,".Rds",sep=""))
```

```{r}
sessionInfo()
```


Tables {data-icon="fa-table"}
=====================================

### Cross-tabulation (confusion matrix) in to what extent each scRNA based cell type is included in each scATAC cluster.

```{r Cross tabulation between scATAC and scRNA based clustering}
scATAC.clusters <- Idents(s.data)
scRNA.clusters <- s.data@meta.data$predicted.id

conf.mat <- table(as.factor(scRNA.clusters),scATAC.clusters)
create_dt(as.data.frame.matrix(conf.mat))
```

---

Cross-tabulation (confusion matrix) in to what extent each scRNA based cell type is included in each scATAC cluster.


### Table format of the top1 gene expression by scATAC cluster dotplot

```{r, message=FALSE}
# create_dt(as.data.frame.matrix(d.plot$data))
```

---

### Top 20 marker genes per scATAC cluster

```{r Top 20 marker genes per scATAC cluster}
# top.20.markers.by.cluster <- e14di.atc.expression.markers %>% group_by(cluster) %>% top_n(n = 20, wt = avg_log2FC)
# create_dt(as.data.frame.matrix(data.frame(top.20.markers.by.cluster)))
```

---

Marker table filtered for convenience having top 20 genes pre cluster

### scRNA cluster marker genes (top20 per cluster)

```{r scRNA cluster marker genes, message=FALSE}
# clean_top20_markers <- read_tsv(scRNA_clean_markers_file)
# create_dt(clean_top20_markers)
```

---
